{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2-layer FNN on MNIST\n",
    "\n",
    "This is MLP (784-200-200-10) on MNIST. Adam algorithm (lr=0.001) with 100 epoches.\n",
    "\n",
    "\n",
    "#### 100 hidden units\n",
    "\n",
    "    Total params: 89,610\n",
    "    Trainable params: 89,610\n",
    "    Non-trainable params: 0\n",
    "\n",
    "####  200 hidden units\n",
    "\n",
    "    Total params: 199,210\n",
    "    Trainable params: 199,210\n",
    "    Non-trainable params: 0\n",
    "\n",
    "####  200 hidden units with 10 intrinsic dim\n",
    "\n",
    "    Total params: 2,191,320\n",
    "    Trainable params: 10\n",
    "    Non-trainable params: 2,191,310\n",
    "    \n",
    "####  200 hidden units with 5000 intrinsic dim    \n",
    "    Total params: 996,254,210\n",
    "    Trainable params: 5,000\n",
    "    Non-trainable params: 996,249,210    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 hiddent units:\n",
      "(0.189223, 0.9799, 0.000443476, 0.99988, 130.689)\n",
      "\n",
      "100 hiddent units:\n",
      "(0.209104, 0.9762, 0.000937227, 0.9997, 67.9818)\n",
      "\n",
      "10 dim:\n",
      "(2.28143, 0.148, 2.2858, 0.15498, 112.831)\n",
      "\n",
      "50 dim:\n",
      "(1.71806, 0.4069, 1.74416, 0.40236, 113.329)\n",
      "\n",
      "100 dim:\n",
      "(1.3122, 0.5669, 1.35212, 0.55128, 116.139)\n",
      "\n",
      "300 dim:\n",
      "(0.609013, 0.8068, 0.643988, 0.79478, 136.209)\n",
      "\n",
      "500 dim:\n",
      "(0.438253, 0.8662, 0.456212, 0.86158, 159.758)\n",
      "\n",
      "1000 dim:\n",
      "(0.293679, 0.9095, 0.296019, 0.91144, 216.802)\n",
      "\n",
      "2000 dim:\n",
      "(0.211655, 0.9352, 0.195016, 0.94168, 351.066)\n",
      "\n",
      "3000 dim:\n",
      "(0.165178, 0.9477, 0.143844, 0.9572, 579.501)\n",
      "\n",
      "4000 dim:\n",
      "(0.14892, 0.954, 0.113969, 0.96642, 870.157)\n",
      "\n",
      "5000 dim:\n",
      "(0.132843, 0.9596, 0.0855529, 0.9755, 960.53)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "results_dir = '../results'\n",
    "\n",
    "\n",
    "class Results():\n",
    "    def __init__(self):\n",
    "        self.train_loss     = []\n",
    "        self.train_accuracy = []\n",
    "        self.train_loss = []\n",
    "        self.valid_loss = []\n",
    "        self.run_time   = []\n",
    "        \n",
    "    def add_entry(self, train_loss, train_accuracy, valid_loss, valid_accuracy, run_time):\n",
    "        self.train_loss.append(train_loss)\n",
    "        self.train_accuracy.append(train_accuracy)\n",
    "        self.train_loss.append(train_loss)\n",
    "        self.valid_loss.append(valid_loss)\n",
    "        self.run_time.append(run_time)\n",
    "      \n",
    "    def add_entry_list(self, entry):\n",
    "        self.add_entry(entry[0], entry[1], entry[2], entry[3], entry[4])\n",
    "        \n",
    "    def list2np(self):\n",
    "        self.train_loss     = []\n",
    "        self.train_accuracy = []\n",
    "        self.train_loss = []\n",
    "        self.valid_loss = []\n",
    "        self.run_time   = []        \n",
    "\n",
    "dim = [10, 50, 100, 300, 500, 1000, 2000, 3000, 4000, 5000]\n",
    "i = 0        \n",
    "\n",
    "# filename list of diary\n",
    "diary_names = []\n",
    "for subdir, dirs, files in os.walk(results_dir):\n",
    "    for file in files:\n",
    "        if file == 'diary':\n",
    "            fname = os.path.join(subdir, file)\n",
    "            diary_names.append(fname)\n",
    "  \n",
    "diary_names_ordered = []\n",
    "for d in dim:\n",
    "    for f in diary_names:\n",
    "        if str(d)+'/' in f:\n",
    "            # print \"%d is in\" % d + f\n",
    "            diary_names_ordered.append(f)\n",
    "        if '_200dir/' in f:\n",
    "            diary_names_dir = f\n",
    "        if '_dir/' in f:\n",
    "            diary_names_dir_100 = f            \n",
    " \n",
    "\n",
    "# extrinsic update  method\n",
    "with open(diary_names_dir,'r') as ff:\n",
    "    lines0 = ff.readlines()\n",
    "    R_dir = extract_num(lines0)\n",
    "\n",
    "with open(diary_names_dir_100,'r') as ff:\n",
    "    lines0 = ff.readlines()\n",
    "    R_dir_100 = extract_num(lines0)\n",
    "    \n",
    "    \n",
    "print \"200 hiddent units:\\n\" + str(R_dir) + \"\\n\"\n",
    "print \"100 hiddent units:\\n\" + str(R_dir_100) + \"\\n\" \n",
    "\n",
    "\n",
    "# intrinsic update method\n",
    "Rs = []\n",
    "i = 0\n",
    "for fname in diary_names_ordered:\n",
    "    with open(fname,'r') as ff:\n",
    "        lines0 = ff.readlines()\n",
    "        R = extract_num(lines0)\n",
    "        print \"%d dim:\\n\"%dim[i] + str(R) + \"\\n\"\n",
    "        i += 1\n",
    "\n",
    "        Rs.append(R)\n",
    "                            \n",
    "Rs = np.array(Rs)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_num(lines0):\n",
    "\n",
    "    valid_loss_str     = lines0[-5]\n",
    "    valid_accuracy_str = lines0[-6]\n",
    "    train_loss_str     = lines0[-8]\n",
    "    train_accuracy_str = lines0[-9]\n",
    "    run_time_str       = lines0[-10]\n",
    "\n",
    "    valid_loss     = float(valid_loss_str.split( )[-1])\n",
    "    valid_accuracy = float(valid_accuracy_str.split( )[-1])\n",
    "    train_loss     = float(train_loss_str.split( )[-1])\n",
    "    train_accuracy = float(train_accuracy_str.split( )[-1])\n",
    "    run_time       = float(run_time_str.split( )[-1])\n",
    "    \n",
    "    return valid_loss, valid_accuracy, train_loss, train_accuracy, run_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance comparison with Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LhuVPeTgwpZRS3saVpC6c7QGP87VYE44XOnUYW9gxONEegLVFVXnqsAeDUkop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\nUjczM8vE/wC+ZbFsqnRaKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4bc6623e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 10\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, Rs[:,0],'b-', label=\"Testing\")\n",
    "ax.plot(dim, R_dir[0]*np.ones(N),'b-', label=\"Testing: baseline\")\n",
    "ax.plot(dim, Rs[:,2],'g-', label=\"Training\")\n",
    "ax.plot(dim, R_dir[2]*np.ones(N),'g-', label=\"Training: baseline\")\n",
    "\n",
    "ax.scatter(dim, Rs[:,0])\n",
    "ax.scatter(dim, Rs[:,2])\n",
    "\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss')\n",
    "ax.set_title('Cross Entropy  Loss')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "ax.set_ylim([-0.1,1.1])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7B0uWeAl+1izYsgXCwmD0aHjkES/Bx8QE4UJERKrgT9J+CvjOzN4BDLgM+HNA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4bc6321b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, Rs[:,1],'b-', label=\"Testing\")\n",
    "ax.plot(dim, R_dir[1]*np.ones(N),'b-', label=\"Testing: baseline\")\n",
    "ax.plot(dim, Rs[:,3],'g-', label=\"Training\")\n",
    "ax.plot(dim, R_dir[3]*np.ones(N),'g-', label=\"Training: baseline\")\n",
    "ax.scatter(dim, Rs[:,1])\n",
    "ax.scatter(dim, Rs[:,3])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Accuracy')\n",
    "ax.set_title('Cross Entropy  Accuracy')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "ax.set_ylim([0.75,1.01])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pOX5gtpnNACZ4y32AWcEJSUTEf99t+I5rxl9DnYp1mN1/NtXKVfM7JJFCkafC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\nbRCeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4bc6321390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, Rs[:,4],'g-', label=\"Training\")\n",
    "ax.plot(dim, R_dir[4]*np.ones(N),'g-', label=\"Training: baseline\")\n",
    "ax.scatter(dim, Rs[:,4])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Time (second)')\n",
    "ax.set_title('Wall Clock Time')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "# ax.set_ylim([0.75,100.01])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Per Dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  2.28143000e-01   1.48000000e-02   2.28580000e-01   1.54980000e-02\n",
      "    1.12831000e+01]\n",
      " [  3.43612000e-02   8.13800000e-03   3.48832000e-02   8.04720000e-03\n",
      "    2.26658000e+00]\n",
      " [  1.31220000e-02   5.66900000e-03   1.35212000e-02   5.51280000e-03\n",
      "    1.16139000e+00]\n",
      " [  2.03004333e-03   2.68933333e-03   2.14662667e-03   2.64926667e-03\n",
      "    4.54030000e-01]\n",
      " [  8.76506000e-04   1.73240000e-03   9.12424000e-04   1.72316000e-03\n",
      "    3.19516000e-01]\n",
      " [  2.93679000e-04   9.09500000e-04   2.96019000e-04   9.11440000e-04\n",
      "    2.16802000e-01]\n",
      " [  1.05827500e-04   4.67600000e-04   9.75080000e-05   4.70840000e-04\n",
      "    1.75533000e-01]\n",
      " [  5.50593333e-05   3.15900000e-04   4.79480000e-05   3.19066667e-04\n",
      "    1.93167000e-01]\n",
      " [  3.72300000e-05   2.38500000e-04   2.84922500e-05   2.41605000e-04\n",
      "    2.17539250e-01]\n",
      " [  2.65686000e-05   1.91920000e-04   1.71105800e-05   1.95100000e-04\n",
      "    1.92106000e-01]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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LjyxX478BPAlMkXQmsFtE3FHyyiqUw97MzPImy9X4XwJuAHZLX9dLOqvUhVUq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\nqy+/iHg+Ih5KP68BHgNG4j7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4bc3616e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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A+6evmyVdWOrCKpXD3szM8qbD59kX+BTwtojYAiDp28DDwA9KWVilctibmVne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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4bc35de510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NRs = Rs/np.array(dim).reshape(N,1)\n",
    "print NRs\n",
    "\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,0],'b-', label=\"Testing\")\n",
    "ax.scatter(dim, NRs[:,0])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)\n",
    "\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,2],'g-', label=\"Training\")\n",
    "ax.scatter(dim, NRs[:,2])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FDgH+1lIC0J3Ky5PTAZ4caGZmpaZgIwERUS/pi8BCoAy4LCKWS/oOsCQi5gOXAldKWgm8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\nLWd9TFrWUp1VkvoDQ4G1be0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IGNxOnWdJrlN+pYVtZZHcirfgJNUAX4uIJelNuM5P4zq0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\n1kf9fxCR66V80VYlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4bc6612f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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6nKS9SI8SjAbWA3dJ+mhE3NqybkTcANwAUFVVFRMnTsxLTNXV1cx6rokV67ey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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4bc3f8c490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,1],'b-', label=\"Testing\")\n",
    "ax.scatter(dim, NRs[:,1])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)\n",
    "\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,3],'g-', label=\"Training\")\n",
    "ax.scatter(dim, NRs[:,3])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mdoG7/6bNdtOB6QAVFRVUV1dH0p66ujreqHkDgOpnqxnSZ0gkx+mp6urqIvu/6Q1Uv/yo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\nWOUkaJPiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4bc3aca9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,4],'g-', label=\"Training\")\n",
    "ax.scatter(dim, NRs[:,4])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Time (second)')\n",
    "ax.set_title('Wall Clock Time')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "# ax.set_ylim([0.75,100.01])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
